BVI-Lowlight：用于低光视频增强的完全配准基准数据集
BVI-Lowlight: Fully Registered Benchmark Dataset for Low-Light Video Enhancement
AI总结：
针对低光视频增强中训练数据稀缺的问题，本文提出包含40个场景、两种低光条件且完全配准的BVI-Lowlight数据集，并通过实验证明其重要性。
摘要：
低光视频通常表现出时空不连贯的噪声，导致可见性差，并在各种计算机视觉应用中损害性能。使用现代技术增强此类内容的一个重大挑战是训练数据的稀缺。本文介绍了一种新颖的低光视频数据集，包含在两种不同低光条件下、多种运动场景中捕获的40个场景，并融入了真实噪声和时间伪影。我们使用可编程电动滑轨在正常光照下捕获完全配准的基准真值数据，随后通过基于图像的后处理对其进行细化，以确保不同光照水平下帧的像素级对齐。本文还对低光数据集进行了详尽的分析，并展示了我们的数据集在监督学习背景下的广泛性和代表性。我们的实验结果表明，完全配准的视频对在开发低光视频增强方法中的重要性，以及进行全面评估的必要性。我们的数据集可在DOI:10.21227/mzny-8c77获取。
英文摘要：
Low-light videos often exhibit spatiotemporal incoherent noise, leading to poor visibility and compromised performance across various computer vision applications. One significant challenge in enhancing such content using modern technologies is the scarcity of training data. This paper introduces a novel low-light video dataset, consisting of 40 scenes captured in various motion scenarios under two distinct low-lighting conditions, incorporating genuine noise and temporal artifacts. We provide fully registered ground truth data captured in normal light using a programmable motorized dolly, and subsequently, refine them via image-based post-processing to ensure the pixel-wise alignment of frames in different light levels. This paper also presents an exhaustive analysis of the low-light dataset, and demonstrates the extensive and representative nature of our dataset in the context of supervised learning. Our experimental results demonstrate the significance of fully registered video pairs in the development of low-light video enhancement methods and the need for comprehensive evaluation. Our dataset is available at DOI:10.21227/mzny-8c77.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
